Looking at Income and Population

Lets look at the City of Hamilton and focus on various demographic parameters. Specifically, population and average income.

# we'll start by loading in the libraries we need

library(leaflet)
library(sp)
library(rgdal)
library(maptools)
library(sf)

#Loading the Hamilton Census Tracts (Also known as Dissemination Areas, or "DA's")

Hamilton_Dissemination_Areas <- readOGR("D:\\Blog_Personal_Work\\Hamilton_Market_Data\\Hamilton_Census_Layers.shp")

# And setting it up to be mapped later 

Ham_Reread_Data <- st_read("D:\\Blog_Personal_Work\\Hamilton_Market_Data\\Hamilton_Census_Layers.shp") %>% 
  st_transform(crs="+init=epsg:4326")



#Just plotting the map normally, without any specific data being shown

plot(Hamilton_Dissemination_Areas)

Analyzing Demographic Data

Now that we have the shapefile properly uploaded, lets look at some variables. We can start by summarizing the data, and visualizing the distribution of the population and income variables

summary(Ham_Reread_Data$Pop_Int)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   433.0   508.5   608.7   641.0  4726.0

hist(Ham_Reread_Data$Pop_Int, main="Distribution of Population Data", xlab = "Population Count")

With the histogram, we can see that the vast majority of the DA’s are under 1000 people, with almost half of them under 500 individuals.

Moving onto income:

summary(Ham_Reread_Data$Income_Int)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   13421   34025   40837   43213   48298  220363      26

hist(Ham_Reread_Data$Income_Int, main="Distribution of Average Income Data", xlab = "Average Income ($)")

And when we look at income, we can see majority of individuals in Hamilton earn under approximately $75,000.

Mapping Demographic Data

Now lets actually create some interactive maps!

#Creating the colour scale 

Population_pal <- colorQuantile("Blues", NULL, n = 5)



# Preparing the text for the map:
mytext=paste("Population: ", Ham_Reread_Data$Pop_Int, sep="")

Hamilton_Pop_Map <- leaflet(Ham_Reread_Data) %>% 
  addProviderTiles(providers$OpenStreetMap) %>%
  setView(-79.8,43.25,10) %>%
  addPolygons(data=Ham_Reread_Data,
              weight = 2,
              opacity = 1,
              color = ~Population_pal(as.integer(Pop_Int)),
              dashArray = "1",
              fillOpacity = 0.7,
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                dashArray = "",
                fillOpacity = 0.7,
                bringToFront = TRUE),
                label = mytext) %>%
  addLegend(pal = Population_pal, values = round(Ham_Reread_Data$Pop_Int,0), labFormat = function(type, cuts, p) {
    n = length(cuts)
    paste0(cuts[-n], " &ndash; ", cuts[-1])
  },  opacity=0.9, title = "Population ", position = "bottomleft"  )
    

Hamilton_Pop_Map

And now we can look at the average income per DA as well:

Income_pal <- colorQuantile("YlGn", NULL, n = 5)

mytext=paste("Average Income: ", Ham_Reread_Data$Income_Int, sep="")

Hamilton_Inc_Map <- leaflet(Ham_Reread_Data) %>% 
  addProviderTiles(providers$OpenStreetMap) %>%
  setView(-79.8,43.25,10) %>%
  addPolygons(data=Ham_Reread_Data,
              weight = 2,
              opacity = 1,
              color = ~Income_pal(as.integer(Income_Int)),
              dashArray = "1",
              fillOpacity = 0.7,
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                dashArray = "",
                fillOpacity = 0.7,
                bringToFront = TRUE),
                label = mytext) %>%
  addLegend(pal = Income_pal, values = round(Ham_Reread_Data$Income_Int,0), labFormat = function(type, cuts, p) {
    n = length(cuts)
    paste0(cuts[-n], " &ndash; ", cuts[-1])
  },  opacity=0.9, title = "Population ", position = "bottomleft"  )
    

Hamilton_Inc_Map

And done!